The Last Iceberg – How Artificial Intelligence Is Unlocking Humanities Deep Frozen Secrets

Tip of the iceberg 90839Icebergs are common meme used throughout the Internet. You see them everywhere, from depicting social media to human behaviors. They are used to explain knowledge we know, above the surface, and those things we don’t know, below the surface. Icebergs are interesting. They’re secretive. The ten percent we see is the literal tip of what is possible. Below the waterline, just out of sight, are dark secrets. Secrets that are often out of the reach and unusable. That is, until now.

Artificial intelligence (AI) is changing our the way we live. AI is doing more than just helping us find patterns in data or helping us make better decisions, it’s truly unlocking unexpected insights and extending our knowledge in ways that only humans are capable of doing… capable of controlling. Prior to AI, human beings had to use their minds to harvest knowledge from everyday life events. It’s a hard process. A process that required countless hours of dedication just to discover one new meaningful insight that can lead to massive improvements in our lives. But this is now changing as we begin to rely on new cognitive technologies that generate knowledge for us…knowledge without us.

Iceberg Breaking Slowing Global WarmingAI is melting those data icebergs. In essence, it is becoming the global warming of the knowledge age. It’s unlocking their deep hidden secrets. AI is unleashing those hidden insights, producing more knowledge that is then used to melts even more icebergs. It’s an exothermic knowledge activity and that is exponentially generating more insights than used in discovery process. And herein lies a devastating, potentially life ending, problem.

As we rely, over rely, on new cognitive technologies, we lose our ability to discover new knowledge ourselves. The brain as an organ and capabilities are lost when not used. Take for example the Slide Ruler. Most people today do not know what a slide ruler is, let alone how to use one. This simple mechanical device, seen in the hands of most engineers hands in the 1970s, can perform amazing mathematics. With just two opposing rulers, one can do addition, subtraction, multiplication, division, logarithms, square roots, n-roots, and more. There’s nothing that can’t be mathematically done on the slider. It requires not batteries, no internet connection, and does not fail. It is brilliant in its complex simplicity. But today nobody knows how to use it. Why?

Figure1 Multiplication C DFor the slide ruler example, we have lost this cognitive ability as a society because it has been outsourced it to other systems, like the calculator and the spreadsheet. These are new productivity tools that were invented to help us more efficiently unlock knowledge. But the cost of using them is that we’re now no longer capable of exercising a part of the brain that used to physically discover insights through this mechanical manipulation. Artificial intelligence is now accelerating this kind of cognitive decay.

As humans rely more on AI to discover knowledge, we are slowly losing our own cognitive ability, our own mental capacity, to discover those insights ourselves. Our brain cognitively weakens. AI is in essence creating a mental defect in our executive functioning processes. Unchecked over time, we will become over dependent on AI to identify those new things that will lead to a better life. Eventually evolving to a point where we could literally die without this AI ability. Or even die because of it.

Main qimg 05a329fbabebe9e44945b8a336201176 cThis uncontrolled release of knowledge can be a destructive chaotic process. We see similar outcome with uranium, for example. With the right equipment one can control how neutrons are absorbed in uranium isotopes, producing a stable reaction which generates life-giving energy. left unchecked, however, the same neutron interacting with the same isotopes can produce devastating nuclear events. Controlled reactions lead to life, and control reactions lead to death.

Can humans survive the chaos of a world where AI is unlocking more knowledge that humans can handle? A future world where available knowledge is greater than the questions we can ask? Physics tells us we cannot. History shows us it is unlikely. AI unchecked, ungoverned, can be the nuclear weapon that we use on ourselves that will eventually melt not only every last iceberg, but society itself.

Cognitive Computing – It Emergent not Design

NewImageI recently read an article “Building Your Cognitive Technology” byTom Davenport. What I find of interest in articles like this a lack of an understandable discussion on what makes cognitive computing cognitive. Yes, they spend countless works describing attributes (data types, capable of learning, transparent, etc.); but very little, if any, on the computational approach for cognitive computing.

Take, for example, the use of Emergent Phenomenon, which is used to achieve highly complex, often cognitive behavior. Emergence is a process through which large complex patterns of behavior (cognitive by nature) can be achieved through the interaction of simpler processes (activities), each of which do not exhibit complex behavior. Think of 10,000 ants swarming to regulate a hive to within 1 degree celsius. Ants have no formal communication, no sight, no central command and control, just pheromones as a mean of marking their trail. Their ability to regulate birthing hives is a type of emergent phenomenon that was not programmed, is just emerges.

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By the way, think about how one would design, implement, and test an emergent system. These systems “behave” a lot like offspring in the developmental stages of life (infants, children, teenagers, young adults, etc.). We do not “design” them, but yet through repeated complex interactions with their environment (people, places, and things), they grown to achieve amazing capabilities.

As such, true cognitive computing is more Emergent that Designed. However, today’s solutions tends to simulate cognitive behavior (fake the behavior without understanding their structure) rather than emulating its capabilities (takes on structural similarities that result in comparable characteristics). A very simplistic example is traditional genetic algorithms, originally developed by Holland. His approach uses a series of zeros and ones to encode information, from which evolutionary principles (selection, crossover, mutation, etc.) are applied. Over a series of evolutions, populations of these strings can exhibit complex behaviors.

NewImageThis, however, is not the way nature works. Instead genetic evolution is encoded in  nucleotides (C, T, A G) through which more complex expressions of value can be made. While a subtle difference, it is that difference between simulation and emulation which holds back true evolutionary-based cognitive progress.

Until such time that our computational approach to solve changes, that is more emulation (emergent) than simulation (design), cognitive computing will mostly be the things through which marketing manipulates the buying masses and not the means through which our silicon-based helper nurtures its human masters.